Ha-Yang Kim’s research while affiliated with Korea Institute of Energy Research and other places

This study estimated the available renewable market potential based on Levelized Cost Of Electricity and then assessed the renewable derived energy self-sufficiency for the unit of local government in South Korea. To calculate energy self-sufficiency, 1 km gridded market renewable generation and local government scale of final energy consumption data were used based on the market costs and statistics for the recent three years. The results showed that the estimated renewable market potentials were 689 TWh (Install capacity 829 GW, 128 Mtoe), which can cover 120% of power consumption. 55% of municipalities can fully replace the existing energy consumption with renewable energy generation and the surplus generation can compensate for the rest area through electricity trade. However, it was confirmed that, currently, 47% of the local governments do not fully consider all renewable energy sources such as wind, hydro and geothermal in establishing 100% renewable energy. The results of this study suggest that energy planning is decentralized, and this will greatly contribute to the establishment of power planning of local governments and close the information gap between the central government, the local governments, and the public.

In order to develop an offshore wind farm by reusing the surplus grid connection capacity after the closure of the Gori Nuclear Power Plant, this paper proposes a method of assessing offshore wind resources with the short-term lidar measurement data obtained from the nearby onshore area. For this purpose, the correction of the lidar measurement error in complex terrain using computational fluid mechanics, the long-term correction using the third-generation reanalysis data, and the wake loss estimation using the wake models were analyzed. Since the lidar measurement was carried out on flat onshore terrain, the error correction was only 0.03 m/s in terms of wind speed MAE, but the final annual energy production showed a 10% difference compared to the uncalibrated results. In particular, it was assessed that the process of long-term correction and climatology transfer of onshore meteorological data to offshore was highly uncertain. If the wind turbines were far enough apart, the conventional wake models for inland wind farm predicted no wake loss, while the deep array wake models predicted a wake loss in the order of 6%. Therefore, it will be necessary to install an offshore meteorological tower and conduct long-term measurements in order to minimize uncertainty in offshore wind power projects.

The Korean Government is promoting the distribution of energy storage systems (ESS) that use new and renewable energy sources by applying the Renewable Energy Certificate (REC) weight factor in relation to the new and renewable energy source. This study estimated the optimal ESS capacity by conducting an economic analysis of the REC weight factor. In the case of solar energy, the weight factors of 5.0 and 4.0 were applied to ESSs set to be charged during the specific time of 10:00-16:00 and due to be installed by 2019 and 2020, respectively. The formula for calculating the optimal capacity is E.C=3.64*P.C-0.49 for a weight factor of 5.0 and 4.0. Here, P.C refers to the solar generator capacity while E.C refers to the optimal ESS capacity. Considering the rate of increase of the charged amount per capacity of the solar power generator installed in Daejeon Metropolitan City, the optimal capacity of the ESS installed in the solar power generator is about 3.6 times that of the solar power generator when the weight factor is 5.0.

Third-generation reanalysis data such as CFSR, ERA-Interim, and MERRA, which have improved spatial resolution and accuracy by assimilating satellite observation data, are widely used for the long-term correction of wind resource assessments. However, there is no obvious criterion for the selection of datasets, and the reported accuracy from actual application cases are all different. In this study, we provide basic information for estimating the uncertainty of reanalysis data selection by reviewing the characteristics of each dataset with a quantitative comparison of three kinds of reanalysis data. The wind speed and wind power density showed significant differences between the reanalysis data, but there was relatively little difference in the Weibull shape factor, which defines wind speed distribution. It was found that wind speed distribution in a low latitude band follows normal distribution rather than a Weibull shape. In conclusion, substantial uncertainty is expected depending on the reanalysis data, and further comparison analysis to establish its application guideline is anticipated.